Bibliographic record
Abstract
Reconciling and Rehumanizing Indigenous-Settler Relations: An Applied Anthropological Perspective presents a unique and honest account of an applied anthropologist’s experience in working with Indigenous peoples of Canada. It illustrates Dr. Nadia Ferrara’s efforts in reconciliation and rehumanization, showing that it is all about recognizing our shared humanity. In this self-reflective narrative, the author describes her personal experience of marginalization and how it contributed to a more in-depth understanding of how others are marginalized, as well as the fundamental sense of belongingness and connectedness. The book is enriched with stories and insights from her fieldwork as a clinician, a university professor, and a bureaucrat. Dr. Ferrara shows how she has applied her experience as an art therapist in Indigenous communities to her current work in policy development to ensure the policies created reflect their current realities. Reconciling and Rehumanizing Indigenous-Settler Relations describes the cultural competency course for public servants Dr. Ferrara is leading, as a means to break down stereotypes and showcase the resilience of Indigenous peoples. She makes a compassionate and urgent call to all North Americans to connect with their responsibility and compassion, and acknowledge the injustices that the original peoples of this land have faced and continue to face. Reconciliation requires concrete action and it starts with the individual’s self-reflection, engagement in authentic human-to-human dialogue, learning from one another, and working together towards a better future, all of which is chronicled in this insightful book.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.081 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".